LIBRE: The Multiple 3D LiDAR Dataset

by   Alexander Carballo, et al.

In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 12 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations. Data captured independently from each sensor includes four different environments and configurations: static obstacles placed at known distances and measured from a fixed position within a controlled environment; static obstacles measured from a moving vehicle, captured in a weather chamber where LiDARs were exposed to different conditions (fog, rain, strong light); dynamic objects actively measured from a fixed position by multiple LiDARs mounted side-by-side simultaneously, creating indirect interference conditions; and dynamic traffic objects captured from a vehicle driven on public urban roads multiple times at different times of the day, including data from supporting sensors such as cameras, infrared imaging, and odometry devices. LIBRE will contribute the research community to (1) provide a means for a fair comparison of currently available LiDARs, and (2) facilitate the improvement of existing self-driving vehicles and robotics-related software, in terms of development and tuning of LiDAR-based perception algorithms.



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